Vehicle control for reducing road wear

ABSTRACT

In a computer-implemented method embodiment of the present invention, a self-driving vehicle (SDV) is steered onto a particular tire path on a roadway based on past taken tire paths. One or more processors determine a pattern of tire path usage by vehicles on a roadway. Based on this previous tire path usage, the processor(s) direct an SDV on-board computer on an SDV to pick at least one second tire path for the SDV to drive thereon based on the pattern of tire path usage by the vehicles on the roadway.

BACKGROUND

The present invention relates generally to the field of vehicles, andspecifically to the field of self-driving vehicles. Still morespecifically, the present invention relates to the field of managingwhere self-driving vehicles travel along roadways.

Self-driving vehicles (SDVs) are vehicles that are able to autonomouslydrive themselves through private and/or public spaces. Using a system ofsensors that detect the location and/or surroundings of the SDV, logicwithin or associated with the SDV controls the speed, propulsion,braking, and steering of the SDV based on the sensor-detected locationand surroundings of the SDV.

One feature of SDVs is that they are able to drive with a level ofprecision that far exceeds that of human drivers. This provides manyadvantages over human-driven vehicles, such as accident avoidance,better gas mileage, the ability to drive closer together (due toreaction times that far exceed those of humans), etc. However, onedrawback to this high level of precision is that SDVs tend to operate ina perfectly uniform manner. That is, when SDVs travel along a same laneon a stretch of road, they all tend to travel exactly in the middle ofthe lane, in order to avoid crowding or hitting vehicles in adjacentlanes. Unfortunately, this uniform location results in the same part ofthe lane always being driven upon, which leads to premature rutting onthe roadway.

SUMMARY

In a computer-implemented method embodiment of the present invention, avehicle is steered onto a particular tire path on a roadway based onpast-taken tire paths. One or more processors determine a pattern oftire path usage by vehicles on a roadway. Based on this previous tirepath usage, the processor(s) direct an on-board computer on the vehicleto pick at least one second tire path for the vehicle to drive on. Byadjusting where the vehicle drives, the roadway experiences less rutting(especially if the roadway is paved) if the vehicle drives along adifferent tire path.

In an embodiment of the present invention, if the roadway is unpaved,the vehicle is directed to take a tire path on the roadway that has beenused most frequently, thus packing down a rut on the unpaved roadway,which leads to better traction for the vehicle.

In another computer-implemented method embodiment of the presentinvention, one or more processors determine a pattern of tire path usageby vehicles on a roadway. The processor(s) then direct a Self-DrivingVehicle (SDV) on-board computer of an SDV to recommend at least one tirepath for the SDV to drive thereon based on the pattern of tire pathusage by the vehicles on the roadway. Thus, the SDV is autonomouslysteered along a tire pathway that minimizes rutting on the roadway.

In another computer-implemented method embodiment of the presentinvention, one or more processors determine a pattern of tire path usageby multiple Self-Driving Vehicles (SDVs) on a roadway. An SDV on-boardcomputer on a present SDV that is currently traveling on the roadwayreceives the pattern of tire path usage by the multiple SDVs that havetraveled on the roadway during a past time period. One or moreprocessors then direct an SDV on-board computer of the present SDV tocontrol the present SDV to travel along at least one tire path based onthe pattern of tire path usage by the multiple SDVs that have previouslytraveled on the roadway. Thus, the present SDV that is traveling on theroadway learns from the other SDVs where the other SDVs have traveled onthe roadway, such that the present SDV picks a tire path that minimizesroadway rutting (in the case of a paved roadway) or packs down a tirepath (in the case of an unpaved roadway).

Other embodiments of the present invention include a computer programproduct and a computer system for implementing some or all of thecomputer-implemented methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary system in accordance with one or moreembodiments of the present invention;

FIG. 2 illustrates an example of a self-driving vehicle (SDV), inaccordance with one or more embodiments of the present invention;

FIG. 3 depicts additional detail of FIG. 2;

FIG. 4 illustrates additional detail of one or more SDVs illustrated inFIGS. 2-3;

FIG. 5 depicts an exemplary blockchain in accordance with one or moreembodiments of the present invention;

FIG. 6 illustrates an exemplary process in accordance with one or moreembodiments of the present invention;

FIG. 7 depicts an exemplary cloud computing environment in accordancewith one or more embodiments of the present invention; and

FIG. 8 depicts exemplary abstraction model layers of a cloud computingenvironment, in accordance with one or more embodiments of the presentinvention.

DETAILED DESCRIPTION

With reference now to the figures, and in particular to FIG. 1, there isdepicted a block diagram of an exemplary system and network inaccordance with one or more embodiments of the present invention. Someor all of the exemplary system and network, including depicted hardwareand/or software, shown for and within computer 101 can be utilized bysoftware deploying server 149 or other systems 155 shown in FIG. 1;and/or monitoring system 301 shown in FIG. 3; and/or SDV on-boardcomputer 401 shown in FIG. 4.

Referring now to FIG. 1, exemplary computer 101 includes processor(s)103, operably coupled to a system bus 105. Although a single processorand core are shown, computer 101 may embody or use multiple processors103, one or more of which may have one or more processor core(s) 123. Avideo adapter 107, which drives/supports a display 109 (which may be atouch screen capable of receiving touch inputs), is also coupled tosystem bus 105. System bus 105 is coupled via a bus bridge 111 to aninput/output (I/O) bus 113. An I/O interface 115 is coupled to I/O bus113. I/O interface 115 affords communication with various I/O devices,including a keyboard 117, a speaker 119, a media tray 121 (which mayinclude storage devices such as CD-ROM drives, multi-media interfaces,etc.), a transceiver 123 that is able to send and transmit wirelesselectronic messages, sensors 153 that are able to identify traffic androadway conditions around a vehicle, and external USB port(s) 125. Whilethe format of the ports connected to I/O interface 115 may be any knownto those skilled in the art of computer architecture, in one or moreembodiments, some or all of these ports are universal serial bus (USB)ports.

As depicted, network interface 129 is also coupled to system bus 105.Network interface 129 can be a hardware network interface, such as anetwork interface card (NIC), etc. Computer 101 is able to communicatewith a software deploying server 149 and/or other systems 155 vianetwork interface 129 and network 127. Network 127 may include (withoutlimitation) one or more external networks—such as a wide area network(WAN), and/or a network of networks such as the Internet—and/or one ormore internal networks such as an Ethernet or a virtual private network(VPN). In one or more embodiments, network 127 includes a wirelessnetwork, such as a Wi-Fi network, and a cellular network. Someembodiments are implemented in a network “cloud” environment, examplesof which will be discussed with reference to FIGS. 7 and 8.

Referring again to FIG. 1, a hard drive interface 131 is also coupled tosystem bus 105. Hard drive interface 131 interfaces with a hard drive133. In one embodiment, hard drive 133 is a non-volatile memory storingand populates a system memory 135 (e.g., random access memory (RAM)),which is also coupled to system bus 105. System memory may be considereda lowest level of volatile memory in computer 101. System memory 135 mayinclude additional, higher levels of volatile memory (not shown),including, but not limited to, cache memory, registers and buffers. Datathat populates system memory 135 includes operating system (OS) 137 andapplication programs 143 of computer 101.

Operating system (OS) 137 includes a shell 139, for providingtransparent user access to resources such as application programs 143.Generally, shell 139 is a program that provides an interpreter and aninterface between the user and the OS. More specifically, shell 139(sometimes referred to as a command processor) can execute commandsentered into a command-line user interface or from a file. In otherwords, shell 139 can serve as a command interpreter. While shell 139 isa text-based, line-oriented user interface, the present invention willequally well support other user interface modes, such as graphical,voice, gestural, etc. As depicted, shell 139 can be considered thehighest level of an OS software hierarchy. The shell can also provide asystem prompt, interpret commands entered by keyboard, mouse, or otheruser input media, and send the interpreted command(s) to the appropriate(e.g., lower) levels of the operating system (e.g., a kernel 141) forprocessing.

As depicted, OS 137 also includes kernel 141, which includes(hierarchically) lower levels of functionality for OS 137. A few(non-limiting) examples of kernel functions include: providing essentialservices required by other parts of OS 137 and application programs 143,including memory management, process and task management, diskmanagement, and mouse and keyboard management.

Application program(s) 143 can include a renderer, shown in exemplarymanner as a browser 145. Browser 145 includes program modules andinstructions (not depicted) enabling a world wide web (WWW) client(e.g., computer 101) to send and receive network messages from network127 (e.g., the Internet using hypertext transfer protocol (HTTP)messaging), thus enabling communication with software deploying server149 and/or other systems 155.

In some embodiments, application program(s) 143 in system memory 135 ofcomputer 101 include one or more software programs for controlling aself driving vehicle (PCSDV) 147 in accordance with the presentinvention. In some embodiments, system memory 135 and/or applicationprogram(s) 143 can be shared/distributed across one or more softwaredeploying servers 149 and/or other systems 155. As depicted, PCSDV 147includes program instructions (software) adapted for implementingprocesses and/or functions in accordance with the present invention,such as those described with reference to FIGS. 2-6. In someembodiments, PCSDV 147 is downloaded from software deploying server 149,(on-demand or “just-in-time”, such that the software in PCSDV 147 is notdownloaded until needed for execution). In one embodiment of the presentinvention, software deploying server 149 performs all of the functionsassociated with the present invention (including execution of PCSDV147), thus freeing computer 101 from having to use its own internalcomputing resources to execute PCSDV 147.

Also within computer 101 is a positioning system 151, which is describedin further detail below.

The elements depicted in computer 101 are not intended to be exhaustive,but rather are representative to highlight components thought to be moreimportant to this example of the present invention. For instance,computer 101 may include alternate memory elements and/or storagedevices such as flash memory, magnetic cassettes, digital versatiledisks (DVDs), Bernoulli cartridges, and the like. These and othervariations are intended to be within the spirit and scope of the presentinvention.

Embodiments of the present invention include a method and system forautomatically positioning a self-driving vehicle (SDV) within a roadlane based on road-input data, thus achieving a certain distribution oflocations on the road through time (e.g. travel paths along and within alane). Controlling where the SDV drives on the road lane results in 1)extending the life of the roadway, and 2) improving the safe operationof the SDV.

SDVs are able to drive with a precision that exceeds manual driving. Assuch, rather than drifting from side to side in a lane (and yet stillremaining within the borders of the lane), an SDV will maintain a paththat is in the same position within the lane every time it travels alongthe roadway. The problem with such precision is that, after a while,certain portions of the lane come into more frequent contact with thetires of the SDV, while other portions of the lane do not. Thisprecision can lead to premature wear (rutting) of portions of the lane.

For example, consider the self-driving vehicle 202 shown traveling onroadway 204 in FIG. 2. Roadway 204 has a roadway surface 206 that liesatop a subgrade 208 (e.g., stabilized soil). As shown in FIG. 2, SDV 202is traveling exactly in the middle of the depicted lane 214 (i.e.,equidistant from the roadway edges 210). By traveling in this exact sametire path over and over, eventually rutted tire paths 212 will occur,due to deformation of the roadway surface 206 and the underlyingsubgrade 208.

FIG. 3 illustrates an example of a self-driving vehicle (SDV), inaccordance with one or more embodiments of the present invention. Withparticular reference now to FIG. 3, an overhead view of SDV 202traveling on lane 214 with its tires in a rutted (worn) tire path 212 isdepicted. While FIG. 2 and FIG. 3 show the rutted tire path 212 asactually being rutted (indented into) the lane 214, in one or moreembodiments of the present invention, formation of the rutted tire path212 is avoided by making the SDV 202 (and other vehicles such as SDV 303and SDV 305) avoid consistently driving just on the area depicted asrutted tire path 212.

In some embodiments of the present invention, roadway sensor(s) 307 areable to assist SDV 202 and/or a monitoring system 301 in determining thecurrent condition of the roadway 204 (and thus lane 214). For example,roadway sensor(s) 307 can include (without limitation): vibrationsensors, thermometers, cameras, strain gauges, microphones, chemicalsensors, pressure sensors, etc., that will generate sensor readings thatindicate roadway data. Examples (without limitation) of such roadwaydata include data about a width of the roadway 204; an amount of trafficon the roadway 204; a speed of traffic on the roadway 204; types ofother vehicles on the roadway 204; weights of other vehicles on theroadway 204; weather conditions on the roadway 204; defects in a surfaceof the roadway 204; the type of material used to construct the roadway204 (e.g., hard materials such as asphalt, concrete, composite pavement,and bituminous surfaces or soft material such as snow, sand, mud, loosegravel, and dirt.).

In one embodiment of the present invention, the type of material used toconstruct the roadway is determined by roadway sensor(s) 307 adapted tobe able to detect the amount of vibration in the roadway 204 whenvehicles pass by. The vibrations may be represented as vibrationsignatures, which can be matched to a particular type of roadwayconstruction material. That is, concrete will present a first vibrationsignature, while asphalt will present a second vibration signature, etc.as vehicles pass over the material. Alternatively, the material used toconstruct roadway 204 may be stored in a database associated withmonitoring system 301. This database can be derived from constructionrecords provided to and/or maintained by local governmental roadwayentities (e.g., departments of transportation at the city, county,state, or federal level).

With reference now to FIG. 4, additional detail of an exemplary SDV 202in accordance with one or more embodiments of the present invention isdepicted. As shown in FIG. 4, an exemplary SDV 202 has an SDV on-boardcomputer 401 that can autonomously control one or more operations of SDV202, and which has been configured to select a specific tire path inaccordance with one or more embodiments of the present invention. SDVon-board computer 401 includes a driving mode device 407 that canprovide directives that selectively allow the SDV 202 to be operated inmanual mode or autonomous mode. In some embodiments, driving mode device407 is a dedicated hardware device that can selectively direct the SDVon-board computer 401 to operate the SDV 202 in an autonomous mode or ina manual mode.

While in autonomous mode, SDV 202 autonomously operates without theinput of a human driver, such that the engine, steering mechanism,braking system, horn, signals, etc. are controlled by the SDV controlprocessor 403, which is under the control of the SDV on-board computer401. For example, the SDV on-board computer 401 operates in autonomousmode based on the processing of information from navigation and controlsensors 409 and driving mode device 407 (indicating that the SDV 202 isto be controlled autonomously). In other words, in autonomous mode,manual driver inputs to the SDV control processor 403 and/or SDVvehicular physical control mechanisms 405 are not needed, as opposed tosemi-autonomous mode in which the SDV on-board computer 407 will “backup” the inputs made by the driver. That is, in autonomous mode, the SDV202 can operate with no human input whereas in semi-autonomous mode, theSDV 202 is controlled by a combination of driver inputs (e.g., forsteering, braking, etc.) along with computer backup (e.g., automaticallybraking the SDV 202 if the driver does not react to an obstacle quicklyenough).

As mentioned, the SDV on-board computer 401 uses information fromnavigation and control sensors 409 to control the SDV 202. Navigationand control sensors 409 may include sensors (hardware and/or software)that: 1) determine the location of the SDV 202 on or relative to aroadway; 2) sense other cars and/or obstacles and/or physical structuresaround SDV 202; 3) measure the speed and direction of the SDV 202;and/or 4) provide any other inputs needed to safely control the movementof the SDV 202.

By way of example only, a location of the SDV 202 can be determinedthrough the use of a positioning system such as positioning system 151shown in FIG. 1. Positioning system 151 may use a global positioningsystem (GPS), which uses space-based satellites that provide positioningsignals that are interpreted by a GPS receiver to determine ageophysical position of the SDV 202. Positioning system 151 may alsouse, either alone or in conjunction with the GPS system, physicalmovement sensors such as accelerometers (which measure acceleration of avehicle in any direction), speedometers (which measure the instantaneousspeed of a vehicle), airflow meters (which measure the flow of airaround a vehicle), etc. Such physical movement sensors may incorporatethe use of semiconductor strain gauges, electromechanical gauges thattake readings from drivetrain rotations, barometric sensors, etc.

Another example of positioning system element of SDV 202 is a LightDetection and Ranging (LIDAR) 433 (shown in FIG. 4) or Laser Detectionand Ranging (LADAR) system which allows the presence and location ofother physical objects to be ascertained by the SDV on-board computer401 by measuring the time it takes to receive back the emittedelectromagnetic radiation (e.g., light), and/or evaluate a Doppler shift(i.e., a change in frequency to the electromagnetic radiation that iscaused by the relative movement of the SDV 202 to objects beinginterrogated by the electromagnetic radiation).

Some embodiments of positioning system 151 may use radar or otherelectromagnetic energy that is emitted from an electromagnetic radiationtransmitter (e.g., transceiver 423 shown in FIG. 4), bounced off aphysical structure (e.g., another car), and then received by anelectromagnetic radiation receiver (e.g., transceiver 423) e.g., tosense or assist the sensing of other cars and/or obstacles and/orphysical structures around SDV 202.

A speed and direction measurement of the SDV 202 may be accomplished bytaking readings from an on-board speedometer (not depicted) on the SDV202 and/or detecting movements to the steering mechanism (also notdepicted) on the SDV 202 and/or the positioning system 151 discussedabove.

Other inputs directed to safe control of the SDV 202 include, but arenot limited to, control signals to activate a horn, turning indicators,flashing emergency lights, etc. on the SDV 202.

In one or more embodiments of the present invention, SDV 202 includesintegrated roadway sensors 411 that are physically coupled to the SDV202. Roadway sensors 411 may include a camera 421 and/or other sensors(heat sensors, moisture sensors, thermometers, etc.) that are able todetect the amount of water, snow, ice, etc. on the roadway 204 shown inFIG. 2 and FIG. 3.

For example, camera 421 may be trained on a roadway 204 that the SDV 202is traveling, in order to provide images of conditions on the roadway204 upon which the SDV 202 is traveling. An object motion detector 419(e.g., a radar transceiver capable of detecting Doppler shiftsindicative of the speed and direction of movement of other vehicles,animals, persons, etc. on the roadway 204) may similarly be trained onthe roadway 204.

In some embodiments of the present invention, SDV 202 includes equipmentsensors 415. SDV equipment sensors 415 may include cameras aimed attires on the SDV 202 to detect how much tread is left on the tire,whether or not the tires are studded (e.g., “snow tires” with metalstuds embedded therein for improved traction, but at the expense ofadditional damage to the roadway surface). SDV equipment sensors 415 mayinclude electronic sensors that detect how much padding is left of brakecalipers on disk brakes. SDV equipment sensors 415 may includedrivetrain sensors that detect operating conditions within an engine(e.g., power, speed, revolutions per minute—RPMs of the engine, timing,cylinder compression, coolant levels, engine temperature, oil pressure,etc.), the transmission (e.g., transmission fluid level, conditions ofthe clutch, gears, etc.), etc. SDV equipment sensors 415 may includesensors that detect the condition of other components of the SDV 202,including lights (e.g., using circuitry that detects if a bulb isbroken), wipers (e.g., using circuitry that detects a faulty wiperblade, wiper motor, etc.), etc. Thus, in one or more embodiments of thepresent invention, if the SDV on-board computer 401 is trying to decidewhich tire path to take, this decision may be impacted by the currentcondition of the SDV 202.

In one or more embodiments of the present invention, SDV 202 includes acommunications transceiver 417, which is able to receive and transmitelectronic communication signals (e.g., radio frequency (RF) messages)from and to other communications transceivers found in other vehicles,servers, monitoring systems, etc.

In one or more embodiments of the present invention, also within SDV 202is a proximity sensor 441, which can use motion detectors, radar (usingDoppler shifting logic), etc., to detect an object (e.g., a vehicle in anext lane) near SDV 202.

In one or more embodiments of the present invention, also within SDV 202is a chemical sensor 427, which can detect the presence of certainchemicals (e.g., oil, gasoline, etc.) on the roadway 204 around the SDV202.

In one or more embodiments of the present invention, also within SDV 202is a microphone 431, which can be used to detect noises (e.g., roadnoise) around the SDV 202, which may indicate roadway surfaceconditions, traffic conditions, etc.

With reference again to FIG. 3, data related to traffic patterns (e.g.,which tire paths have been taken, traffic volume, etc.) and/or roadwayconditions (e.g., weather, rutting, etc.) can be shared between SDVs202, 303, 305 to estimate the amount of wear existing on the roadway204.

In some embodiments, information may be shared from a blockchain thatthe SDVs 202, 303, 305 maintain, which can improve security (so that anSDV cannot be “hacked”). An example of a blockchain will be discussed inmore detail with reference to FIG. 5.

SDV 303 may use known technical art to visually detect ruts in roadway204. This information may be shared between SDVs so as to shape orencourage future road distribution driving patterns for SDVs. By way offurther example, through the aggregated data/information of SDVsdescribed herein, certain road wear patterns may be estimated. In someembodiments, an SDV may make use of road wear pattern estimateinformation (“W”) for sections of a road. W may be communicated amongvehicles (e.g., the SDVs), and a historical account of estimated roadwear may be created (e.g., storing W for a section of road in ablockchain). Blockchain technology may then be used to create a robust,tamper-proof, and always-available means for storing such information.In some embodiments, W may also contain information regarding road type(including estimated surface material) and other parameters, which maybe used when planning roadway construction, positioning of SDVs, etc.

While information may be written to a blockchain in a continuous manner,in some embodiments, the blockchain is written to in response to certainevents. For example, once more than a predetermined number of SDVs(e.g., more than 1000) pass over a certain path (e.g., rutted tire path212 on roadway 204), this will trigger the blockchain to be updatedto 1) reflect the number of SDVs that have passed over that certain pathand/or 2) update the physical deterioration that has occurred to therutted tire path 212 since the blockchain was last written to (beforethe 1000 SDVs passed over the rutted tire path).

With reference now to FIG. 5, an exemplary blockchain as used andmaintained by multiple SDVs to store information about the state ofroadway 204 is presented. Information stored in the blockchainmaintained by these multiple SDVs may include a record of the pathstaken by cars, the speed and number of cars on the roadway 204, recordsof communications between the SDVs, etc. As shown in FIG. 5, computers501, 502, 503, 504, 505, and 506 (e.g., SDV on-board computers 401 foundin various SDVs) represent an exemplary peer-to-peer network of devicesused to support a peer blockchain (in which more or fewercomputers/machines may form the peer-to-peer network of devices). Eachof the computers 501, 502, 503, 504, 505 and 506 (which may includetelecommunication devices) of the peer-to-peer network has a copy ofdata (e.g., data that represents telecommunication device events), asheld in ledgers stored within the blockchains 508, 509, 510 that areassociated with respective computers 504, 505, 506.

As shown in FIG. 5, a client 507 (e.g., a telecommunication device thatinitiates a blockchain transaction) sends a transaction Tx (e.g., anevent that occurred at the telecommunication device) to the client'speer (depicted as computer 501). Computer 501 then sends the transactionTx to the corresponding ledgers of blockchains 508, 509, 510 associatedwith other peers, e.g., computers 502, 503 504, 505, 506.

Blocks within exemplary blockchain 508 are depicted as block 511, block512, and block 513. In this example, block 513 can be a newest entryinto a ledger held in blockchain 508, and includes not only the newesttransactions but also a hash of the data from older block 512, whichincludes a hash of even older block 511. Thus, oldest blocks are madeeven more secure each time a new block is created, due to the hashingoperations.

Referring again to the example of FIG. 5, assume that the peer-to-peernetwork employs a consensus model and computer 505 has been designatedas a leader peer L according to such a consensus model. As such, theleader peer (computer 505) organizes all transactions from thenodes/peers/computers/telecommunication devices 501-506, and then sharesnew blocks/transactions (Tx) with other nodes (e.g., blockchains 515 and517 shown associated with respective computers 502, 503) as depicted.The nodes/computers that receive the new block/transaction (Tx) thenvalidate the new block/transaction. In some embodiments, if enough(i.e., some predefined quantity/percentage) of the nodes/computersvalidate the new block/transaction, then the new block/transaction isdeemed valid for the entire peer-to-peer network of computers 501-506and Tx is added (as valid) to the blockchains (including the depictedblockchains 508, 509, 510) associated with all of thenodes/peers/computers 501-506. Thus, the blockchain system depicted inFIG. 5 provides additional security for past data regarding how othervehicles have traveled along a roadway, as well as addition security forcontrolling directions to on-board SDV computers used to controls SDVs.

FIG. 6 illustrates an exemplary process in accordance with one or moreembodiments of the present invention.

After initiator (start) block 602, the process proceeds to block 604. Inblock 604, one or more processors (e.g., within monitoring system 301and/or an SDV on-board computer 401) determine a pattern of tire pathusage by vehicles on a roadway (e.g., roadway 204). The term “pattern oftire path usage” is defined as a historical pattern of where tires havebeen rolling along the roadway 204. That is, by using a history ofroadway sensor readings taken by roadway sensor(s) 307 and/or bypositioning information generated by a positioning system 151 in one ormore vehicles, a determination can be made as to where vehicular traffichas traveled along the roadway 204, and more specifically the lateraldistance from the sides of a lane 214 that such vehicles have beentraveling. In some embodiments, each SDV selects a tire path that isoffset from other SDVs, so that all of the SDVs do not drive on the sametire path, with a goal of minimizing the possibility of damage toroadway 204.

In block 606, one or more processors direct a self-driving vehicle (SDV)on-board computer (e.g., SDV on-board computer 401 shown in FIG. 4) ofan SDV (e.g., SDV 202) to pick one tire path (e.g., the least worn) forthe SDV to drive thereon based on the pattern of tire path usage by thevehicles on the roadway. The term “tire path” is defined as a path onwhich a tire can roll along the roadway 204. If the same tire path isused repeatedly, then a rutted tire path 212 may result. If the roadway204 is a paved roadway, then a rutted tire path 212 is undesirable,since its continued use may cause permanent damage to the roadway 204.However, if roadway 204 is an unpaved roadway, then a rutted tire path212 may be desirable, since packed down tire ruts (e.g., on a dirt orsnowy road) can provide improved traction.

For example, assume that the roadway is paved, and that there aremultiple tire paths that can be taken (i.e., multiple paths that are tothe left of, the right of, or in the middle of the lane 214). In orderto avoid road rutting (i.e., creating the rutted tire path 212 shown inFIG. 2), the one or more processors will direct the SDV on-boardcomputer to select a distribution of the multiple tire paths for the SDVto drive thereon, such that a same tire path is not always taken by theSDV while traveling along the lane 214. That is, the SDV on-boardcomputer will drive on a path that is offset to the rutted tire path212. Each next SDV will drive on a path that is offset in a differentmanner from the rutted tire path 212, such that no particular path willbe driven over in a consistent manner that is damaging to the roadway204.

However, if the roadway is unpaved (e.g., with a deep layer of snow ordirt on top of the roadway 204), then the rutted area may provide thebest traction, and thus a most frequently used tire path is selected asthe tire path for the SDV to drive thereon.

However, if the roadway is paved, then the SDV will be directed to avoidthe rutted tire path by driving on other adjacent areas of the lane 214.

In an embodiment of the present invention, if the SDV is riding onstudded tires (e.g., snow tires with metal studs for better traction),and the SDV is on a paved road (that is not snow-covered), the SDV willbe directed to adjust its path selection accordingly (e.g., by movingback and forth in a certain stretch of roadway, in order to avoiddigging into the paved roadway in the same area).

In one or more embodiments of the present invention, roadway conditionsmay be received by one SDV from other SDVs or other vehicles (includingthose that support a blockchain). The received information may be usedby the SDV's on-board computer to adjust which tire path is chosen, suchthat an SDV that is currently driving on the roadway can learn fromother SDVs where they have driven, such that the current SDV can adjustwhere it travels accordingly (e.g., in order to avoid rutting theroadway by traveling on the same tire paths as the other SDVs).

The flow-chart in FIG. 6 ends at terminator block 608.

In one or more embodiments of the present invention, a distributed orcentral cloud resource is used to store and manage driving and road wearpatterns on a road. A municipality may maintain such a facility toreceive information about planned or past lane positioning targets fromSDVs. In this way, a distribution of road wear predictions may bemaintained by the municipality. Points of aggregation of these data maybe 1) toll booths, 2) EZ-pass communication hubs, 3) central facilityconnected to the Internet by which SDVs communicate their positions, 4)GPS navigation hubs (such as electronic maps) which can build up roadwear expectancy maps based on data shared from the client/SDV sideduring navigation.

Furthermore, in one or more embodiments of the present invention,vehicle properties (e.g., weight, speed, axle dimensions, number of andarrangement of wheels, etc.) are shared along the section of road, inaddition to lane position data, in order to create a weighted histogramof expected wear and tear on the lane. In this way, the weighted laneposition histogram can be constructed and serve as a better resource formunicipal planning and road maintenance execution.

In some embodiments, a command can be received from the municipality tocontrol future lane positioning of SDVs. Because aggregation ofpositioning data and command relays are required locally for eachstretch of road or highway, the construction of road wear aggregatedistributions may be conducted in a distributed network of sensors alonga road.

Furthermore, the processes described herein (i.e., detecting and/orpredicting where rutting on the roadway will occur) may enable a roadrepair control and planning facility that is coupled to this cloudresource to predict where repairs are needed.

Some embodiments of the present invention thus facilitate a drivepattern optimization for reduced road wear. In some embodiments, the carpattern mean location and standard deviation may be controlled andshaped. If the distribution of car traversal paths are considered to beakin to “noise”, then various noise characteristics may be considered orencouraged, at least along a portion of a lane near the center of thelane, such as: White noise, which has constant power spectral density;Gaussian noise, with a probability density function equal to that of anormal distribution; Pink noise, with a spectral density inverselyproportional to frequency; Brownian noise or “brown” noise, with aspectral density inversely proportional to the square of frequency.

By way of further example, Gaussian white noise can describe adistribution of tire traversal paths along just a portion of a lane.Consider FIG. 3 as an example of a Gaussian distribution of road wear,caused by a tight distribution of traversal paths centered on the middleof a driving lane. When high wear patterns are detected, theself-driving vehicles/cars could be instructed to shift either left orright resulting in wearing out a different section of the roadway. Whenthe road wears out at those positions, the cars can shift again, suchthat more uniform road wear occurs compared to the individual Gaussiandistributions.

The present invention may be implemented in one or more embodimentsusing cloud computing. Nonetheless, it is understood in advance thatalthough this disclosure includes a detailed description on cloudcomputing, implementation of the teachings recited herein is not limitedto a cloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 7, an illustrative cloud computing environment 50is depicted. As shown, cloud computing environment 50 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as the Private, Community,Public, or Hybrid clouds described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-54Nshown in FIG. 7 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and self-driving vehicle control processing96, which performs one or more workloads and functions in accordancewith the present invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of various embodiments of the present invention has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the present invention in theform disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the present invention. The embodiment was chosen and describedin order to best explain the principles of the present invention and thepractical application, and to enable others of ordinary skill in the artto understand the present invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

Some embodiments of the present invention may be implemented through theuse of a VHDL (VHSIC Hardware Description Language) program inconjunction one or more compatible electronic devices (sometimesreferred to as VHDL chip). VHDL is an exemplary design-entry languagefor electronic devices such as Field Programmable Gate Arrays (FPGAs),Application Specific Integrated Circuits (ASICs), and other devices. Byway of further example only, a computer implemented method (embodied insoftware) be emulated by a hardware-based VHDL program, which is thenapplied to a VHDL chip, such as a FPGA.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Having thus described embodiments of the present invention of thepresent application in detail and by reference to illustrativeembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of the presentinvention defined in the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:determining, by one or more processors, a pattern of tire path usage byvehicles on a roadway; directing, by one or more processors, an on-boardcomputer of a vehicle to recommend at least one tire path for thevehicle to drive thereon based on the pattern of tire path usage by thevehicles on the roadway, wherein the vehicle is a self-driving vehicle(SDV); determining, by one or more processors, that the SDV is riding onstudded tires; and adjusting, by one or more processors, which at leastone tire path is chosen based on the SDV riding on the studded tires. 2.The computer-implemented method of claim 1, wherein the at least onetire path comprises multiple tire paths, and wherein thecomputer-implemented method further comprises: determining, by one ormore processors, that the roadway is paved; and directing, by one ormore processors, an SDV on-board computer on the SDV to select adistribution of the multiple tire paths of a paved roadway for the SDVto drive thereon.
 3. The computer-implemented method of claim 2, whereinsaid determining, by one or more processors, that the roadway is paved,further comprises determining, by one or more processors, that theroadway is constructed of materials selected from a group consisting ofasphalt, concrete, composite pavement, and bituminous surfaces.
 4. Thecomputer-implemented method of claim 1, wherein the computer-implementedmethod further comprises: identifying, by one or more processors,rutting in at least one tire path from the pattern of tire path usage bythe vehicles on the roadway; and directing, by one or more processors,an SDV on-board computer on the SDV to pick a tire path that is lessrutted than said at least one tire path for the SDV to drive thereon. 5.The computer-implemented method of claim 1, wherein the at least onetire path for the SDV is picked based on at least one factor from agroup consisting of: a type of road material used to construct theroadway; a width of the roadway; an amount of traffic on the roadway; aspeed of traffic on the roadway; types of other vehicles on the roadway;weights of other vehicles on the roadway; weather conditions on theroadway; and defects in a surface of the roadway.
 6. Thecomputer-implemented method of claim 1, wherein the computer-implementedmethod further comprises: receiving, by an SDV on-board computer on theSDV, road condition reports about the roadway from multiple other SDVson the roadway; and adjusting, by the SDV on-board computer on the SDV,which at least one tire path is chosen based on road condition reportsabout the roadway from multiple other SDVs on the roadway.
 7. Thecomputer-implemented method of claim 6, further comprising: receiving,by the SDV on-board computer on the SDV, the road condition reports froma blockchain that is supported by the multiple other SDVs.
 8. Thecomputer-implemented method of claim 1, wherein the computer-implementedmethod is executed as a cloud-based service.
 9. A computer programproduct comprising a non-transitory computer readable storage mediumhaving program instructions embodied therewith, the program instructionsreadable and executable by a processor to cause the processor to:determine a pattern of tire path usage by vehicles on a roadway; directan on-board computer of a vehicle to recommend at least one tire pathfor the vehicle to drive thereon based on the pattern of tire path usageby the vehicles on the roadway, wherein the vehicle is a self-drivingvehicle (SDV); determine that the SDV is riding on studded tires; andadjust which at least one tire path is chosen based on the SDV riding onthe studded tires.
 10. The computer program product of claim 9, whereinthe at least one tire path comprises multiple tire paths, and whereinthe program instructions are further readable and executable by theprocessor to cause the processor to: determine that the roadway ispaved; and direct an SDV on-board computer on the SDV to select adistribution of the multiple tire paths of a paved roadway for the SDVto drive thereon.
 11. The computer program product of claim 9, whereinthe program instructions are provided as a service in a cloudenvironment.
 12. A system comprising: one or more processors; one ormore computer readable memories operably coupled to the one or moreprocessors; wherein the one or more computer readable memories haveprogram instructions stored thereon for execution by at least one of theone or more processors, the program instructions comprising: programinstructions to determine a pattern of tire path usage by vehicles on aroadway; program instructions to direct an on-board computer of avehicle to recommend at least one tire path for the vehicle to drivethereon based on the pattern of tire path usage by the vehicles on theroadway, wherein the vehicle is a self-driving vehicle (SDV); programinstructions to determine that the SDV is riding on studded tires; andprogram instructions to adjust which at least one tire path is chosenbased on the SDV riding on the studded tires.
 13. The system of claim12, wherein the at least one tire path comprises multiple tire paths,and wherein the program instructions further comprise: programinstructions to determine that the roadway is paved; and programinstructions to direct an SDV on-board computer on the SDV to select adistribution of the multiple tire paths of a paved roadway for the SDVto drive thereon.
 14. The system of claim 12, wherein the programinstructions are provided as a service in a cloud environment.